Exploring alternative spatial weights to detect crash hotspots

Ulak, Mehmet Baran; Özgüven, Eren Erman; Vanli, O. Arda; Horner, Mark W.

doi:10.1016/j.compenvurbsys.2019.101398

Cited by 21 publications

(16 citation statements)

References 30 publications

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“…The Prediction Accuracy Index (PAI) [58], firstly proposed by Chainey et al, is the ratio of the hit rate to the fraction of area covered [59]. The PAI has been widely applied to measure hotspot analysis results [17,[59][60][61]. Previously, the Crash Prediction Accuracy Index (CPAI) was developed by using road length rather than area for evaluating traffic crash hotspot analysis performance [17,61].…”

Section: The Intersection Prediction Accuracy Index (Ipai)mentioning

confidence: 99%

“…The PAI has been widely applied to measure hotspot analysis results [17,[59][60][61]. Previously, the Crash Prediction Accuracy Index (CPAI) was developed by using road length rather than area for evaluating traffic crash hotspot analysis performance [17,61]. Based on the previous studies, the Intersection Prediction Accuracy Index (IPAI) was developed in this research with road network restraints for evaluating IHA prediction performance as follows:…”

Section: The Intersection Prediction Accuracy Index (Ipai)mentioning

confidence: 99%

“…A review of previous studies shows that the Getis-Ord Gi*, well known in hotspot analysis, has been commonly used to detect crash hotspots [15]. Hotspot analysis examines the Getis-Ord Gi* statistic [16,17], a local indicator of spatial autocorrelation developed by Professor Arthur Getis and J. Keith Ord, for individual crashes based on a comparison with neighboring crashes to quantitatively This paper presents a three-step approach to directly identifying crash hotspot intersections (CHIs) by using (1) construction of the inverse network distance-band spatial weights matrix of intersection (INDSWMI), (2) construction of the k-nearest distance-band spatial weights matrix between crash and intersection (KDSWMCI), and (3) intersection hotspot analysis (IHA). The process map for the approach is shown in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

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A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices

2020

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In this paper we develop a new approach to directly detect crash hotspot intersections (CHIs) using two customized spatial weights matrices, which are the inverse network distance-band spatial weights matrix of intersections (INDSWMI) and the k-nearest distance-band spatial weights matrix between crash and intersection (KDSWMCI). This new approach has three major steps. The first step is to build the INDSWMI by forming the road network, extracting the intersections from road junctions, and constructing the INDSWMI with road network constraints. The second step is to build the KDSWMCI by obtaining the adjacency crashes for each intersection. The third step is to perform intersection hotspot analysis (IHA) by using the Getis–Ord Gi* statistic with the INDSWMI and KDSWMCI to identify CHIs and test the Intersection Prediction Accuracy Index (IPAI). This approach is validated by comparison of the IPAI obtained using open street map (OSM) roads and intersection-related crashes (2008–2017) from Spencer city, Iowa, USA. The findings of the comparison show that higher prediction accuracy is achieved by using the proposed approach in identifying CHIs.

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Section: The Intersection Prediction Accuracy Index (Ipai)mentioning

confidence: 99%

Section: The Intersection Prediction Accuracy Index (Ipai)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices

2020

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“…The literature highlights the various methods used to identify crash hotspots and the most common methods are the Moran's I and Getis-Ord statistics [47]. Each method has its own characteristics that are suited to different problems [48]. The local Moran's I index, also referred to as the local indicators of spatial association (LISA) method, measures the extent of an observation's value similarity or difference from its neighboring observations [42], which generalizes the idea underlying the Getis-Ord Gi and Getis-Ord Gi* statistics, as indicators of local pockets of nonstationary areas or hotspots [49].…”

Section: Introductionmentioning

confidence: 99%

“…Additionally, kernel density estimation (KDE) is instrumental in visually highlighting clusters of crashes on a roadway network. Nevertheless, KDE fails to test the statistical significance for the identified hotspots, which is a key drawback of the method [48].…”

Section: Introductionmentioning

confidence: 99%

Identification of Hotspot Segments with a Risk of Heavy-Vehicle Accidents Based on Spatial Analysis at Controlled-Access Highway

et al. 2021

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Significant risk factors that influence the occurrence of heavy vehicle accidents have been explored in numerous studies in order to lower injury severity in traffic accidents. It is imperative to explore road sections with a high risk of heavy vehicle accident occurrence by considering the significant consequences of such accidents for road users, despite the low number of heavy vehicles in traffic flow. To address this, this study proposes a method to predict clustering hotspots for heavy vehicle accidents on the basis of three different criteria, namely, heavy vehicle accident cases, the number of heavy vehicles involved, and accident severity index values. Moran’s I spatial autocorrelation was employed to identify the clustering for each criterion, and the Getis–Ord Gi* statistic was applied to estimate the likelihood of risk along the network. This study considers the features of hotspot points at significance levels from 0.10 to 0.01 with a 1355 m buffer radius to create segments for each criterion. The three criteria for hotspots were considered within the overlapped buffer zone. A total of 22 heavy vehicle risk segments (HVRSs) were identified and then ranked by crash rate. Overall, this study demonstrates the application of different criteria to identify accident hotspots involving a specific vehicle type, which could help in prioritizing segments with a high risk of heavy vehicle accidents, as well as providing information for HVRSs for the purpose of developing appropriate countermeasures for the identified accident hotspots.

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Rough-graph-based hotspot detection of polygon vector data

Tabarej

Minz

2023

Multimed Tools Appl

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Exploring alternative spatial weights to detect crash hotspots

Cited by 21 publications

References 30 publications

A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices

A New Approach to Identifying Crash Hotspot Intersections (CHIs) Using Spatial Weights Matrices

Identification of Hotspot Segments with a Risk of Heavy-Vehicle Accidents Based on Spatial Analysis at Controlled-Access Highway

Rough-graph-based hotspot detection of polygon vector data

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